import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						Union[str, Any]=-1				):





    """simple docstring"""


    UpperCamelCase			  =      label_idx







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Union[Split, str]				):





    """simple docstring"""


    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      mode.value
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   f"""{mode}.txt"""				)
    UpperCamelCase			  =      1
    UpperCamelCase			  =      []
    with open(lowerCamelCase_   ,   encoding="""utf-8"""				) as f:
     UpperCamelCase			  =      []
     UpperCamelCase			  =      []
     for line in f:
      if line.startswith("""-DOCSTART-"""				) or line == "" or line == "\n":
       if words:
        examples.append(InputExample(guid=f"""{mode}-{guid_index}"""   ,   words=lowerCamelCase_   ,   labels=lowerCamelCase_				)				)
        guid_index += 1
        UpperCamelCase			  =      []
        UpperCamelCase			  =      []
      else:
       UpperCamelCase			  =      line.split(""" """				)
       words.append(splits[0]				)
       if len(lowerCamelCase_				) > 1:
        labels.append(splits[self.label_idx].replace("""\n"""   ,   """"""				)				)
       else:
        # Examples could have no label for mode = "test"
        labels.append("""O"""				)
     if words:
      examples.append(InputExample(guid=f"""{mode}-{guid_index}"""   ,   words=lowerCamelCase_   ,   labels=lowerCamelCase_				)				)
    return examples







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						TextIO   ,   lowerCamelCase_						:						TextIO   ,   lowerCamelCase_						:						List				):





    """simple docstring"""


    UpperCamelCase			  =      0
    for line in test_input_reader:
     if line.startswith("""-DOCSTART-"""				) or line == "" or line == "\n":
      writer.write(lowerCamelCase_				)
      if not preds_list[example_id]:
       example_id += 1
     elif preds_list[example_id]:
      UpperCamelCase			  =      line.split()[0] + """ """ + preds_list[example_id].pop(0				) + """\n"""
      writer.write(lowerCamelCase_				)
     else:
      logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,   line.split()[0]				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    if path:
     with open(lowerCamelCase_   ,   """r"""				) as f:
      UpperCamelCase			  =      f.read().splitlines()
     if "O" not in labels:
      UpperCamelCase			  =      ["""O"""] + labels
     return labels
    else:
     return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						List[str]				):





    """simple docstring"""


    super().__init__(label_idx=-2				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    if path:
     with open(lowerCamelCase_   ,   """r"""				) as f:
      UpperCamelCase			  =      f.read().splitlines()
     if "O" not in labels:
      UpperCamelCase			  =      ["""O"""] + labels
     return labels
    else:
     return [
         "O",
         "B-ADVP",
         "B-INTJ",
         "B-LST",
         "B-PRT",
         "B-NP",
         "B-SBAR",
         "B-VP",
         "B-ADJP",
         "B-CONJP",
         "B-PP",
         "I-ADVP",
         "I-INTJ",
         "I-LST",
         "I-PRT",
         "I-NP",
         "I-SBAR",
         "I-VP",
         "I-ADJP",
         "I-CONJP",
         "I-PP",
     ]





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Union[Split, str]				):





    """simple docstring"""


    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      mode.value
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   f"""{mode}.txt"""				)
    UpperCamelCase			  =      1
    UpperCamelCase			  =      []

    with open(lowerCamelCase_   ,   encoding="""utf-8"""				) as f:
     for sentence in parse_incr(lowerCamelCase_				):
      UpperCamelCase			  =      []
      UpperCamelCase			  =      []
      for token in sentence:
       words.append(token["""form"""]				)
       labels.append(token["""upos"""]				)
      assert len(lowerCamelCase_				) == len(lowerCamelCase_				)
      if words:
       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""   ,   words=lowerCamelCase_   ,   labels=lowerCamelCase_				)				)
       guid_index += 1
    return examples







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						TextIO   ,   lowerCamelCase_						:						TextIO   ,   lowerCamelCase_						:						List				):





    """simple docstring"""


    UpperCamelCase			  =      0
    for sentence in parse_incr(lowerCamelCase_				):
     UpperCamelCase			  =      preds_list[example_id]
     UpperCamelCase			  =      """"""
     for token in sentence:
      out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
     out += "\n"
     writer.write(lowerCamelCase_				)
     example_id += 1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    if path:
     with open(lowerCamelCase_   ,   """r"""				) as f:
      return f.read().splitlines()
    else:
     return [
         "ADJ",
         "ADP",
         "ADV",
         "AUX",
         "CCONJ",
         "DET",
         "INTJ",
         "NOUN",
         "NUM",
         "PART",
         "PRON",
         "PROPN",
         "PUNCT",
         "SCONJ",
         "SYM",
         "VERB",
         "X",
     ]

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
